We implement ARVo in PyTorch [1]}.
We adopt a three-layer correlation pyramid to achieve a trade-off between video deblurring quality and computational efficiency.
We use PWC-Net [2]} for optical flow estimation considering its robustness in real-life domains.
During the training, we feed five consecutive frame patches of 256\(\times \) 256 resolution to the model and optimize it using Adam optimizer [3]}. We set the initial learning rate as 10\(^{-4}\)  and reduce it by half every 200 epochs. We also use random flipping and rotations to augment the training dataset.
The weights are shared across all the progressive stages for a compact model.
